_base_ = "./yolov3_d53_mstrain-608_273e_coco.py"
# dataset settings
img_norm_cfg = dict(mean=[0, 0, 0], std=[255.0, 255.0, 255.0], to_rgb=True)
train_pipeline = [
    dict(type="LoadImageFromFile", to_float32=True),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(type="PhotoMetricDistortion"),
    dict(
        type="Expand",
        mean=img_norm_cfg["mean"],
        to_rgb=img_norm_cfg["to_rgb"],
        ratio_range=(1, 2),
    ),
    dict(
        type="MinIoURandomCrop",
        min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
        min_crop_size=0.3,
    ),
    dict(type="Resize", img_scale=(320, 320), keep_ratio=True),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="Pad", size_divisor=32),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]),
]
test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(
        type="MultiScaleFlipAug",
        img_scale=(320, 320),
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=True),
            dict(type="RandomFlip"),
            dict(type="Normalize", **img_norm_cfg),
            dict(type="Pad", size_divisor=32),
            dict(type="ImageToTensor", keys=["img"]),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]
data = dict(
    train=dict(pipeline=train_pipeline),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline),
)
